This thesis investigates the application of machine learning techniques to predict rotor temperature in induction motors, a crucial parameter for enhancing the safety, efficiency, and longevity of electric machines. It focuses on the development, training, and evaluation of three neural network models: Multilayer Perceptron (MLP), Thermal Neural Network (TNN), and 1-D Convolutional Neural Network (1-D CNN). These models are assessed for their ability to predict rotor temperature using measurable inputs such as rotor speed, slip speed, current, voltage, and cooling conditions, both with and without stator temperature measurements.
Experimental results show that the TNN model, which incorporates heat-transfer principles, outperforms other models in terms of accuracy and reliability. The analysis is based on a comprehensive dataset comprising 2,876,126 data points collected over approximately 82 hours, covering various operational scenarios represented by 87 profile IDs. The models were trained on rotor temperatures ranging from 21.6°C to 259.6°C, with 22°C representing idle room temperature and 250°C the rotor's critical threshold.
The findings suggest that machine learning models offer a more adaptable and precise approach to temperature estimation in electric motors, with significant potential to improve operational efficiency and safety protocols in the automotive and industrial sectors. The TNN model was particularly effective, achieving mean squared errors of 4.2 and 23.3 in two different normal driving scenarios. Additionally, including motor housing temperature data, both inside and outside, significantly enhanced rotor temperature prediction accuracy, potentially reducing costs by eliminating the need for stator temperature sensors.
Data collection and preprocessing were done in collaboration with Volvo Cars. Including motor housing temperature significantly improved rotor temperature estimation accuracy, suggesting cost savings by eliminating the need for stator temperature sensors. This research highlights the superiority of ML models over deterministic models for predicting EM temperature, enhancing safety, efficiency, and reliability. The TNN model’s versatility in various driving conditions sets the groundwork for future ML integration into real-time Motor Control Systems.
This research demonstrates the superiority of machine learning models over deterministic models for induction motor temperature prediction, improving safety, efficiency, and reliability. The TNN model's ability to accurately predict rotor temperature under various conditions highlights the potential for integrating machine learning into real-time motor control systems, setting the stage for future advancements in this field.